How does DDPG algorithm know about my action mapping function?
I would recommend doing is allowing your network to output any real number and then clipping the output. For instance, I was working with an agent that had to learn an angle between $[0, 2\pi]$ and $[0, 1]$. If the network outputted e.g. 10 in the first dimension then this would just be clipped to $2\pi$.
This way the agent only learns about actions within the action space and the weights of the network would eventually be adjusted to only output actions within this action space, provided that the boundaries aren't the optimal actions.